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We present a novel language representation model enhanced by knowledge called ERNIE (Enhanced Representation through kNowledge IntEgration).
Cross-lingual language model pretraining
Guillaume Lample and Alexis Conneau. 2019 · 1901
Earlier work this paper cites.
Multi-task deep neural networks for natural language understanding
Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao. 2019 · 1901
Earlier work this paper cites.
“cloze procedure”: A new tool for measuring readability
Wilson L Taylor. 1953 · 1953
Earlier work this paper cites.
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Xin Liu, Qingcai Chen, Chong Deng, Huajun Zeng, Jing Chen, Dongfang Li, and Buzhou Tang. 2018 · 1962
Earlier work this paper cites.
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Ellen M Voorhees. 2001 · 2001
Earlier work this paper cites.
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Earlier work this paper cites.
Efficient estimation of word representations in vector space
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Earlier work this paper cites.
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Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
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Universal sentence encoder
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Chnsenticorp
TAN Song-bo
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Alex Wang, Amapreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. 2018 · 2018
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Learning semantic textual similarity from conversations
Yinfei Yang, Steve Yuan, Daniel Cer, Sheng Yi Kong, Noah Constant, Petr Pilar, Heming Ge, Yun Hsuan Sung, and Brian Strope. 2018 · 2018
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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